青藏高原观测对2020年长江中下游地区梅雨降水过程预报的影响OA北大核心

The Qinghai-Xizang Plateau plays a significant role in influencing China's climate and weather pat-terns.Howe ver,the regional observation density is considerably lower than that of eastern China.Despite this spar-sity,observations from the Qinghai-Xizang Plateau can significantly affect weather forecasts for downstream areas,such as the middle and lower reaches of the Yangtze River.With advancements in the resolution of regional models,many operational forecasting systems in eastern China exclude the Qinghai-Xizang Plateau,leaving the impact of its observations on downstream forecast quality unclear.This study uses the CMA-MESO rapid updating cycle system to simulate the Meiyu weather event that occurred in early July 2020 in the middle and lower Yan-gtze River region.The study evaluates the influence of missing observations from the Qinghai-Xizang Plateau on the accuracy of forecasts for downstream weather systems.Results indicate that the absence of Qinghai-Xizang Plateau observations significantly degrades the 24-72-hour forecasts for surface 2-meter temperature and 10-meter wind fields in the middle and lower Yangtze River.It also negatively affects precipitation forecasts during the first three days.Analysis of the observational impact highlights that Qinghai-Xizang Plateau observations pri-marily influence the middle and lower Yangtze River region,with the largest variance in the 24-72-hour forecast signal occurring in these areas.Thus,improving forecast quality in the middle and lower Yangtze River requires accounting for upstream observational data from the Qinghai-Xizang Plateau and the spatiotemporal propagation of this.To enhance severe weather forecasting in eastern China,it is crucial to not only increase local observation infrastructure,such as radars and weather stations,but also to deploy additional radars and satellites in the Qinghai-Xizang Plateau.Incorporating three-dimensional observational profiles,such as soundings,and deploying mobile observation stations in regions prone to sudden localized heavy precipitation can significantly improve forecast quality.Additionally,to mitigate the limitations of finite-region models,approaches such as high-low reso-lution nesting,variable-resolution modeling,or blending analyses methods should be adopted to incorporate up-stream information.